The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty

Karnuta, Jaret M.; Churchill, Jessica L.; Haeberle, Heather S.; Nwachukwu, Benedict U.; Taylor, Samuel A.; Ricchetti, Eric T.; Ramkumar, Prem N.

doi:10.1016/j.jse.2020.04.009

Cited by 51 publications

(42 citation statements)

References 20 publications

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“…assessed the capability of artificial neural networks to predict length of stay, discharge disposition, and inpatient charges for primary anatomic, reverse, and hemi-shoulder arthroplasty. 18 This model predicted inpatient costs with an accuracy ranging from 69% to 77%, as well as discharge disposition and length of stay with fair to good accuracy (72%-75% and 78%-92%, respectively). Future ML models may provide physicians with the ability to offer an evidence-based, patient-specific tool that preoperatively communicates value metrics for valuable discourse in terms of expectation management and reimbursement arbitration from payor preauthorization.…”

Section: Applications: Ai In the Sports Medicine Literaturementioning

confidence: 99%

Clinical and Research Medical Applications of Artificial Intelligence

Ramkumar

Kunze

Haeberle

et al. 2021

Arthroscopy: The Journal of Arthroscopic & Related Surgery

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Artificial intelligence (AI), including machine learning (ML), has transformed numerous industries through newfound efficiencies and supportive decision-making. With the exponential growth of computing power and large datasets, AI has transitioned from theory to reality in teaching machines to automate tasks without human supervision. AI-based computational algorithms analyze "training sets" using pattern recognition and learning from inputted data to classify and predict outputs that otherwise could not be effectively analyzed with human processing or standard statistical methods. Though widespread understanding of the fundamental principles and adoption of applications have yet to be achieved, recent applications and research efforts implementing AI have demonstrated great promise in predicting future injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting telehealth. With appreciation, caution, and experience applying AI, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. The purpose of this review is to discuss the pearls, pitfalls, and applications associated with AI.

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Section: Applications: Ai In the Sports Medicine Literaturementioning

confidence: 99%

Clinical and Research Medical Applications of Artificial Intelligence

Ramkumar

Kunze

Haeberle

et al. 2021

Arthroscopy: The Journal of Arthroscopic & Related Surgery

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“…AI has been used to help decide whether to perform surgery and to preoperatively estimate the risk of mortality and postoperative complications, thereby furnishing surgeons and patients with more accurate data for making better decisions [42] and providing better informed consent. A study from Kamuta et al [43] employed artificial neural networks to analyze data from 111,147 patients who underwent total shoulder prosthesis and reverse shoulder prosthesis to predict length of stay, discharge status and inpatient costs, showing good accuracy and reliability.…”

Section: Application In Orthopedic Surgerymentioning

confidence: 99%

Artificial intelligence in musculoskeletal conditions

Román-Belmonte

Corte‐Rodríguez

Rodríguez-Merchán

2021

Front. Biosci. (Landmark Ed)

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Artificial intelligence (AI) is an iterative process by which information is captured, transformed into knowledge and processed to produce adaptive changes in the environment. AI is a broad concept, involving virtual (computing) and physical (robotics) elements. In this narrative literature review, we focus on the aspects of AI that present major opportunities for developing health care. Within a few years, AI will be part of our daily clinical practice. Although significant advances are being made, the application of AI in musculoskeletal medicine is still in its early stages compared with its implementation in other areas of medicine. AI is increasingly being employed in fields such as musculoskeletal radiology, skeletal trauma, orthopedic surgery, physical and rehabilitation medicine and sports medicine, as well as for “big data” and AI in gastrointestinal (GI) endoscopy related injuries. Among the limitations of IA are that it analyzes information based on the data it is supplied, which must therefore be well-labeled and that some algorithms such as DL uses more time, data, and computational power than other techniques. Moreover, AI currently does not solve the problem of causality that exists in medicine with observational data; information that physicians interpret within a broad clinical context. AI should therefore be integrated in a prudent and reasonable manner into the workflows of health professionals.

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“…Machine learning has demonstrated considerable potential in the context of value-based health care [12]. By developing advanced prognostic tools for outcome prediction, machine learning overcomes the limitations inherent in traditional regression analyses such as dependence on predefined relationships and collinearity [13][14][15][16]. As such, machine learning is particularly poised to be applied to predictive models in orthopedic surgery.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty

et al. 2021

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Background: Despite reasonable accuracy with preoperative templating, the search for an optimal planning tool remains an unsolved dilemma. The purpose of the present study was to apply machine learning (ML) using preoperative demographic variables to predict mismatch between templating and final component size in primary total knee arthroplasty (TKA) cases. Methods: This was a retrospective case-control study of primary TKA patients between September 2012 and April 2018. The primary outcome was mismatch between the templated and final implanted component sizes extracted from the operative database. The secondary outcome was mismatch categorized as undersized and oversized. Five supervised ML algorithms were trained using 6 demographic features. Prediction accuracies were obtained as a metric of performance for binary mismatch (yes/no) and multilevel (undersized/correct/oversized) classifications. Results: A total of 1801 patients were included. For binary classification, the best-performing algorithm for predicting femoral and tibial mismatch was the stochastic gradient boosting model (area under the curve: 0.76/0.72, calibration intercepts: 0.05/0.05, calibration slopes: 0.55/0.7, and Brier scores: 0.20/0.21). For multiclass classification, the best-performing algorithms had accuracies of 83.9% and 82.9% for predicting the concordance/mismatch of the femoral and tibial implant, respectively. Model predictions of greater than 51.0% and 47.9% represented high-risk thresholds for femoral and tibial sizing mismatch, respectively. Conclusions: ML algorithms predicted templating mismatch with good accuracy. External validation is necessary to confirm the performance and reliability of these algorithms. Predicting sizing mismatch is the first step in using ML to aid in the prediction of final TKA component sizes. Further studies to optimize parameters and predictions for the algorithms are ongoing.

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The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty

Cited by 51 publications

References 20 publications

Clinical and Research Medical Applications of Artificial Intelligence

Clinical and Research Medical Applications of Artificial Intelligence

Artificial intelligence in musculoskeletal conditions

Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty

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